Coverage Path Planning For Multi-view SAR-UAV Observation System Under Energy Constraint

TL;DR

The paper tackles coverage path planning for a swarm of SAR-UAVs under energy constraints, formulating the CPP as maximizing $M_{\text{covered}}/(M \cdot K)$ while enforcing $E_F d_i + E_I M_{i-\text{covered}} \le E_{\max}$ and ensuring one-viewpoint-per-UAV without leaving any target unobserved. It introduces viewpoint generation around targets with a geometric model and an exact optimization framework, then presents Adaptive Density Peak Clustering (ADPC) to reduce problem complexity by clustering viewpoints based on density and distance. The core contribution is the ADPC framework with center selection via $\gamma=\rho \delta$, adaptive clustering via $\sigma$ balancing distance to base and cluster size, and PSO-based optimal path generation. Experimental results show ADPC-PSO achieves higher coverage rates and robust performance under increasing task loads, with computation times in a practical range for swarm missions.

Abstract

Multi-view Synthetic Aperture Radar (SAR) imaging can effectively enhance the performance of tasks such as automatic target recognition and image information fusion. Unmanned aerial vehicles (UAVs) have the advantages of flexible deployment and cost reduction. A swarm of UAVs equipped with synthetic aperture radar imaging equipment is well suited to meet the functional requirements of multi-view synthetic aperture radar imaging missions. However, to provide optimal paths for SAR-UAVs from the base station to cover target viewpoints in the mission area is of NP-hard computational complexity. In this work, the coverage path planning problem for multi-view SAR-UAV observation systems is studied. First, the coordinate of observation viewpoints is calculated based on the location of targets and base station under a brief geometric model. Then, the exact problem formulation is modeled in order to fully describe the solution space and search for optimal paths that provide maximum coverage rate for SAR-UAVs. Finally, an Adaptive Density Peak Clustering (ADPC) method is proposed to overcome the additional energy consumption due to the viewpoints being far away from the base station. The Particle Swarm Optimization (PSO) algorithm is introduced for optimal path generation. Experimental results demonstrate the effectiveness and computational efficiency of the proposed approach.

Figures (4)

• Figure 1: Geometric model of viewpoint generation $\left(K=3\right)$.
• Figure 2: Optimal paths for SAR-UAVs generated by different CPP methods.
• Figure 3: Average coverage rate obtained by CPP methods when different numbers of SAR-UAVs are used.
• Figure 4: Average coverage rate obtained by CPP methods when different numbers of observation viewpoints are used.